Remote blood pressure monitoring with a wearable photoplethysmographic device in patients undergoing coronary angiography: the senbiosys substudy.

AIMS: The purpose of this study is to evaluate the accuracy of the Senbiosys device in measuring blood pressure (BP) by photoplethysmography (PPG) in patients undergoing coronary angiography. METHODS: This is a substudy within the Senbiosys trial, which is a prospective, single-arm, single-center study, evaluating the accuracy of BP estimation of the Senbiosys device compared to invasive BP. Patients referred for coronary angiography underwent invasive BP measurement and simultaneously wore the Senbiosys ring. SBP and DBP estimations measured by the Senbiosys device were compared with invasive measurements. RESULTS: A total of 25 patients were included. Overall, 708 epochs with adequate PPG signal belonging to 17 patients were analyzed. A total of 84% of the SBP estimates and 99% of the DBP estimates have an absolute error of less than 10 mmHg compared with the invasive measurements. Mean difference was 2.3 ± 7.0 mmHg and 0.5 ± 3.5 mmHg for SBP and DBP, respectively. CONCLUSION: The Senbiosys device is accurate enough to determine BP in a selected population undergoing coronary angiography.

Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression.

In this work, we present a photoplethy smography-based blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 clean 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets with a total of 2677 clean 30-second epochs to build the MLR model of the algorithm. For the SBP, a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.90, p = .001. For the DBP, and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.85, p .001. We also use a binary classifier for the BP values with the positives indicating SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.

PPG-Based Respiratory Rate Monitoring Using Hybrid Vote-Aggregate Fusion Technique.

In this work, we present a low-complexity photoplethysmography-based respiratory rate monitoring (PPG-RRM) algorithm that achieves high accuracy through a novel fusion method. The proposed technique extracts three respiratory-induced variation signals, namely the maximum slope, the amplitude, and the frequency, from the PPG signal. The variation signals undergo time domain peak detection to identify the inter-breath intervals and produce three different instantaneous respiratory rate (IRR) estimates. The IRR estimates are combined through a hybrid vote-aggregate fusion scheme to generate the final RR estimate. We utilize the publicly available Capnobase data-sets [1] that contain both PPG and capnography signals to evaluate our RR monitoring algorithm. Compared to the reference capnography IRR, the proposed PPG-RRM algorithm achieves a mean absolute error (MAE) of 1.44 breaths per minute (bpm), a mean error (ME) of 0.70±2.54 bpm, a root mean square error (RMSE) of 2.63 bpm, and a Pearson correlation coefficient r = 0.95, p < .001.

Photoplethysmography Based Blood Pressure Monitoring Using the Senbiosys Ring.

In this work, we evaluate the accuracy of our cuffless photoplethysmography based blood pressure monitoring (PPG-BPM) algorithm. The algorithm is evaluated on an ultra low power photoplethysmography (PPG) signal acquired from the Senbiosys Ring. The study involves six male subjects wearing the ring for continuous finger PPG recordings and non-invasive brachial cuff inflated every two to ten minutes for intermittent blood pressure (BP) measurements. Each subject performs the required recordings two to three times with at least two weeks difference between any two recordings. In total, the study includes 17 recordings 2.21 ± 0.89 hours each. The PPG recordings are processed by the PPG-BPM algorithm to generate systolic BP (SBP) and diastolic BP (DBP) estimates. For the SBP, the mean difference between the cuff-based and the PPG-BPM values is -0.28 ± 7.54 mmHg. For the DBP, the mean difference between the cuff-based and the PPG-BPM values is -1.30 ± 7.18 mmHg. The results show that the accuracy of our algorithm is within the 5 ± 8 mmHg ISO/ANSI/AAMI protocol requirement.

Ear and Finger PPG Wearables for Night and Day Beat-to-Beat Interval Detection.

In this work, we study the accuracy of ear and finger photoplethysmography (PPG) based inter-beat interval (IBI) detection and estimation compared to the R-to-R interval (RRI) values derived from the electrocardiography (ECG). Seven male subjects with a mean age of 34.29±5.28 years are asked to wear simultaneously the Senbiosys earbud SBE2200 and the Senbiosys ring SBF2200 together with the Shimmer3 ECG development kit. The study includes 43 recordings with a total duration of 72.21 hours divided into 37.10 and 35.11 hours of sleep and wake recordings, respectively. The obtained results show that the earbud PPG enables a higher beat detection rate and a more accurate IBI estimation than the ring. They also show that the performance of the beat detection and estimation is significantly better for the sleep recordings compared to the wake recordings with an increase of ∼ 1.5% in the detection rate and a decrease of ∼ 1 ms and ∼ 4 ms in the mean absolute error (MAE) and the root mean square error (RMSE), respectively. Moreover, we propose a novel fusion scheme that smartly combines the IBI values from both devices and achieves a superior performance with a beat detection rate of 99.22% and an IBI estimation with MAE and RMSE values of 7.42 ms and 13.45 ms, respectively.